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diffReps: Detecting Differential Chromatin Modification Sites from ChIP-seq Data with Biological Replicates

Overview of attention for article published in PLOS ONE, June 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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3 X users
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1 Facebook page
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1 Wikipedia page
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1 Google+ user

Citations

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333 Dimensions

Readers on

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290 Mendeley
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4 CiteULike
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Title
diffReps: Detecting Differential Chromatin Modification Sites from ChIP-seq Data with Biological Replicates
Published in
PLOS ONE, June 2013
DOI 10.1371/journal.pone.0065598
Pubmed ID
Authors

Li Shen, Ning-Yi Shao, Xiaochuan Liu, Ian Maze, Jian Feng, Eric J. Nestler

Abstract

ChIP-seq is increasingly being used for genome-wide profiling of histone modification marks. It is of particular importance to compare ChIP-seq data of two different conditions, such as disease vs. control, and identify regions that show differences in ChIP enrichment. We have developed a powerful and easy to use program, called diffReps, to detect those differential sites from ChIP-seq data, with or without biological replicates. In addition, we have developed two useful tools for ChIP-seq analysis in the diffReps package: one for the annotation of the differential sites and the other for finding chromatin modification "hotspots". diffReps is developed in PERL programming language and runs on all platforms as a command line script. We tested diffReps on two different datasets. One is the comparison of H3K4me3 between two human cell lines from the ENCODE project. The other is the comparison of H3K9me3 in a discrete region of mouse brain between cocaine- and saline-treated conditions. The results indicated that diffReps is a highly sensitive program in detecting differential sites from ChIP-seq data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 290 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 4%
France 3 1%
United Kingdom 3 1%
Germany 2 <1%
Italy 1 <1%
Sweden 1 <1%
Mexico 1 <1%
Australia 1 <1%
China 1 <1%
Other 3 1%
Unknown 263 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 89 31%
Researcher 69 24%
Student > Bachelor 24 8%
Student > Master 19 7%
Professor > Associate Professor 15 5%
Other 44 15%
Unknown 30 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 39%
Biochemistry, Genetics and Molecular Biology 67 23%
Neuroscience 21 7%
Computer Science 17 6%
Medicine and Dentistry 10 3%
Other 24 8%
Unknown 39 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2016.
All research outputs
#4,582,085
of 22,712,476 outputs
Outputs from PLOS ONE
#62,630
of 193,919 outputs
Outputs of similar age
#39,765
of 197,423 outputs
Outputs of similar age from PLOS ONE
#1,224
of 4,566 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 193,919 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 197,423 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 4,566 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.